2D Quantile-Quantile Plots

You can visually check for the fit of a theoretical distribution
to the observed data by examining the quantile-quantile (or Q-Q) plot.
In this plot, the observed values of a variable are plotted against the
theoretical quantiles. A good fit of the theoretical distribution to the
observed values would be indicated by this plot if the plotted values
fall onto a straight line. To produce a Q-Q plot, STATISTICA first sorts
the N observed data points into ascending order, so that:

x1 ≤
x2 ≤ ... ≤ xn

These observed values are plotted against one axis of the
graph; on the other axis the plot will show:

F-1((i-radj) / (n+nadj))

where i is the rank of the respective observation,
radj
and nadj
are adjustment factors (≤ 0.5) and F -1
denotes the inverse of the probability integral for the respective standardized
distribution. The resulting plot is a scatterplot of the observed values
against the (standardized) expected values, given the respective distribution.

Note that, in addition to the inverse probability integral value, STATISTICA also shows the respective
cumulative probability values on the opposite axis, that is, the plot
shows not only the standardized values for the theoretical distribution,
but also the respective p-values.
Note also that the adjustment factors radj and nadj
ensure that the p-value for the
inverse probability integral will fall between 0 and 1, but not including
0 and 1 (see Chambers, Cleveland, Kleiner, and Tukey, 1983; in STATISTICA,
the default value for both adjustment factors is 1/3=.333).